Method and apparatus for analyzing amniotic fluid

ABSTRACT

Methods and spectra for monitoring fetal growth and predicting birth weight of an infant prior to birth are provided wherein one or more selected biological markers are measured in a sample of amniotic fluid obtained from a pregnant woman. Levels of the selected biochemical markers and/or spectra correlate with one or more medical conditions, such as fetal growth and birth weight of the infant, and gestational diabetes. A measurement probe for in situ measurement can be used safely and repeatedly. Monitoring and/or treatment of maternal and fetal health is also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of U.S. provisional patent applicationSer. No. 60/496,884 filed Aug. 21, 2003, the content of which is herebyincorporated by reference.

US FEDERAL GOVERNMENT SPONSORED RESEARCH

The inventions described and claimed in this application are not basedon research funded by the US federal government or its agencies, and theUS government has no rights to this patent application.

FIELD OF THE INVENTION

The present invention relates to methods and apparatus for determiningfetal health or a risk of developing a fetal health condition. Theinvention also relates to methods and apparatus for monitoring maternalhealth. The invention further relates to methods and apparatus foranalyzing amniotic fluid to determine one or more biological markers.

BACKGROUND OF THE INVENTION

The assessment of fetal birth weight forms an important part of prenatalcare. Therefore accurate early determination of fetal weight prior todelivery could markedly improving perinatal outcomes. Thus there is aneed for a quick and easy method for estimating fetal weight in-uteroparticularly infants at risk of for either of the two extremes: (1)macrocosmia (also referred to as large for gestational age or LGA) or(2) small for gestational age (SGA) or intrauterine growth retarded(IUGR).

Currently the most reliable predictor of infant birth weight isultrasonography where according to a recent review article it is capableof predicting birth weight to within 300 to 400 grams (Table 12, NahumeMedicine Journal), but the authors cited that this as well as othertechniques still have significant degrees of inaccuracy and suggestedthat a reasonable strategy for arriving at estimated fetal weight isstill to use multiple estimates based on different sources of clinicaland sonographic information. Moreover they noted that even withultrasound, macrosomia is not easily predicted. Both ultrasonography andclinical palpation of fetal size have sensitivities of less than 60% forthe prediction of macrosomia with false positives far greater than 40%.Likewise for small fetuses less than 1800 grams ultrasonic fetal weightestimates are often in error by as much as 25%. The disadvantages ofultrasonography include the complicated and labor intensive nature ofthe methodology that is often limited by the suboptimal visualization offetal organs. It also requires costly equipment and highly trainedpersonnel. The latter requisites often preclude use of any of currenttechniques in developing countries.

The use of ultrasound measurements of the fetus and information aboutthe mother are combined to determine birth weight in WO2004/036359published on 29 Apr. 2004.

Current American and Canadian guidelines recommend that all pregnantwomen be screened for gestational diabetes mellitus (GDM) between 24-28weeks. Prior screening occurs only if multiple risk factors such asolder maternal age, higher pre-pregnancy weight, membership in a highrisk ethnic group or strong family history of diabetes exist or ifprevious diagnosis of GDM or delivery of macrosomic infant haveoccurred. However, several studies acknowledge that ‘selectivescreening’ based on these criteria can still result in under-diagnosisof GDM.

Current screening and diagnostic criteria for GDM are predicated on theobservation that an abnormal oral glucose tolerance test (OGTT), withits accompanying gestational hyperglycemia, increases both perinatal andadult morbidity and mortality. It is argued that the increased flux ofglucose across the placenta is the stimulus for the in-utero productionof insulin from developing pancreatic islet cells and is theprecondition for fetal hyperinsulinism, which in turn leads to increasedfetal abdominal circumference, macrosomia, obesity and neonatalhypoglycemia and the diagnosis of GDM.

SUMMARY OF THE INVENTION

According to one broad aspect of the invention, amniotic fluid isanalyzed in situ without disrupting the amniotic sac.

According to another broad aspect of the invention, amniotic fluid isanalyzed without altering the composition of the amniotic fluid, so asto ascertain information about concentration and/or other components ofthe matrix making up the fluid.

According to yet another broad aspect of the invention, analysis ofamniotic fluid is correlated with a risk of developing a medicalcondition in at least one of a mother and her offspring.

According to a further broad aspect of the invention, prediction ofbirth weight from amniotic fluid analysis is improved either byproviding prediction earlier during pregnancy or by providing betteraccuracy in the prediction.

The invention provides a method of analyzing amniotic fluid in which adevice is provided for measuring one or more selected biological markersin amniotic fluid, and is arranged with respect to an amniotic sac tomeasure amniotic fluid in situ without insertion of any instrument intothe amniotic sac. The device is used to acquire measurement data that isprocessed to obtain a value for one or more selected biological markersin the amniotic fluid.

The invention provides an apparatus for analyzing amniotic fluid in situin a pregnant patient having an amniotic sac containing amniotic fluidwithout insertion of any instrument into the amniotic sac. The apparatuscomprises a device for measuring one or more selected biological markersin amniotic fluid, a coupler adapted to arrange the device with respectto the amniotic sac to measure the amniotic fluid in situ withoutinsertion of any instrument into the amniotic sac, and a processing unitfor processing measurement data from the device to obtain a value forthe one or more selected biological markers in the amniotic fluid.

The invention provides a method of treating at least one of pregnantmother and her fetus by providing a device for measuring one or moreselected biological markers in amniotic fluid, arranging the device withrespect to an amniotic sac to measure amniotic fluid in situ withoutinsertion of any instrument into the amniotic sac, using the device toacquire measurement data, processing the measurement data to obtain avalue for the one or more selected biological markers in the amnioticfluid, and determining at least one of a dietary change and apharmaceutical intervention in response to the value.

The invention provides a method of predicting a risk of developing amedical condition in at least one of a mother and her offspring byproviding a device for analyzing amniotic fluid of the mother, using thedevice to acquire analytical data from the amniotic fluid, andprocessing the analytical data to obtain a prediction value for therisk.

The invention provides an apparatus for predicting a risk of developinga medical condition in at least one of a mother and her offspring. Theapparatus comprises a device for analyzing amniotic fluid, and aprocessing unit for processing analytical data from the device to obtaina prediction value for the risk.

The present invention may be applied to any animal having an amnioticfluid sac that allows for the fluid to be accessed for analysis, and inparticular, the invention may be applied to humans. The terms “patient”,“mother”, “offspring”, “fetus” and other similar terms relating tosubjects and their body parts are intended herein to relate to humansand non-humans, unless otherwise explicitly indicated.

In this specification, the term “medical condition” means a conditionthat is, or has a probability to be, related to the health of mother,fetus or offspring. One example is weight of the offspring at the timeof birth, namely birthweight, weight of the fetus such assmall-for-gestational-age (SGA) or intrauterine growth retarded (IUGR),appropriate-for-gestational-age (AGA) or healthy, andlarge-for-gestational-age (LGA) or macrosomic. Abnormal weight isrecognized be the cause of a variety of health complications. “Medicalcondition” also includes an indication of an absence of a problem suchas AGA, which information to a pregnant woman is a reassurance having abenefit to the well-being of the mother. Another example is diabetes inthe mother, otherwise known as maternal diabetes and more specifically,gestational diabetes mellitus (GDM). A further example is prematurity ofbirth.

In this specification, the term “biological marker” includes one or morebiochemical indices such as glucose, lactate or another metabolic acid,one or more proteins that include but are not limited to insulin,insulin like growth factors (IGFs) and their binding proteins and/or oneor more fatty acids. A biological marker may also comprise cells thatcan be identified and counted within the amniotic fluid, as well asother physical properties of amniotic fluid, such as viscosity, that canbe measured, whether in vitro or in situ.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood by way of the following detaileddescription of several embodiments with reference to the appendeddrawings, in which:

FIG. 1 a illustrates the typical spectrum and wavelength regionsselected for birthweight estimation using NIR Raman measurement ofamniotic fluid for the broad range of birthweight ranging from 1 kg to5.3 kg;

FIG. 1 b illustrates the correlation fit between estimated birthweightand actual birthweight for the sample population for the broad range ofbirthweight ranging from 1 kg to 5.3 kg;

FIG. 1 c illustrates the typical spectrum and wavelength regionsselected for birthweight estimation using NIR Raman measurement ofamniotic fluid for the lower range of birthweight ranging from 1 kg to3.5 kg;

FIG. 1 d illustrates the correlation fit between estimated birthweightand actual birthweight for the sample population for the lower range ofbirthweight ranging from 1 kg to 3.5 kg;

FIG. 1 e illustrates the typical spectrum and wavelength regionsselected for birthweight estimation using NIR Raman measurement ofamniotic fluid for the higher range of birthweight ranging from 3.5 kgto 5.3 kg;

FIG. 1 f illustrates the correlation fit between estimated birthweightand actual birthweight for the sample population for the higher range ofbirthweight ranging from 3.5 kg to 5.3 kg;

FIG. 2 illustrates an endo-vaginal optical Raman spectrometer operatingat the NIR or IR range;

FIG. 3 is a block diagram of the apparatus according to the embodimentof FIG. 2;

FIG. 4 is illustrates schematically an abdominal probe using an opticalabsorption spectrometer;

FIG. 5 is a block diagram of the apparatus according to the embodimentof FIG. 4;

FIG. 6 is a probability contour plot of developing gestational diabetesmellitus (GDM) as a function of glucose level in amniotic fluid (X-axis)and insulin (Y-axis);

FIG. 7 is a probability contour plot of developing gestational diabetesmellitus (GDM) as a function of glucose level in amniotic fluid (X-axis)and intrauterine growth factor binding protein 1 or IGF-BP1 (Y-axis);and

FIG. 8 is a flow chart illustrating the method of treating GDM accordingto the fifth embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS First Embodiment—NIR Raman SpectralAnalysis of In Vitro Amniotic Fluid and Correlation with Birthweight inHumans

One embodiment of the invention focuses on the use of Raman spectralanalyses to identify a panel of 8-12 biochemical markers in amnioticfluid that are predictive of infant birth weight. Advantages of ourapproach are (1) that it requires a single sample of a small volume ofamniotic fluid (μL) to measure all important biochemical componentssimultaneously and thus importantly preserves their chemical propertieswithin the fluid matrix of amniotic fluid, which in and of itself isimportant barometer of fetal health as either too little(oligiohydramniois) or too much (polyhydramniois) is a fetal healthrisk. This overcomes limitations of other chemical techniques thatrequire separate analyses of individual components, which not only aresusceptible to concentration differences if volume is perturbed, but tolack of techniques to measure components in this new compartment forwhich assays in small volumes have not yet been developed. However moreimportantly, our Raman spectral analysis is accurate to within 100 to400 grams of final birth weight when performed as early as 15 wksgestation. This thus provides the first medical possibility of earlyin-utero diagnosis of SGA and LGA. Moreover the methodology can beperformed at the time of routine amniocentesis and does not requireadditional labor-intensive chemical processing of samples. The method ofthe present embodiment can be easily conducted in the hospital, clinicand field setting with the development of two machines: one requiringuse of a small portable Raman spectrometer to measure amniotic fluiddroplets at the time of collection of ‘fresh samples’ for immediatebedside processing and (2) the development of an endo-vaginal or anabdominal fibre optic probe to be used non invasively throughout thecourse of pregnancy providing for the first time a means to collectseries measurements and to monitor in-utero fetal growth and developmentsequentially The feasibility of the method of the present embodimentmakes the possibility of more widespread use of amniotic fluid forroutine fetal monitoring possible and affordable and with an accuracyfar exceeding current techniques.

The present applicants have identified several components of amnioticfluid suitable for measurement. These include but are not limited toglucose, a family of proteins including but not limited to insulin andtwo IGF binding proteins, namely IGF BP 1 and 3, several amino acids,and two metabolic acids (lactic acid and uric acid). Other componentsfor measurement include nitric oxide and several fatty acids includingthe trans fatty acids which are only found in highly hydrogenated foodproducts and that could in fact limit the use of the essential fattyacids required for fetal growth.

Amniotic Fluid NIR-RAMAN Spectroscopy

Amniotic fluid from 68 women at 14-16 weeks gestation, were measured.All patients signed McGill University a Human Subjects Approved form forconsent. After genetic testing, all remaining amniotic samples werestored frozen. Near Infrared Raman spectral were obtained using a BrukerFourier Transform Near Infrared Raman Spectrometer. Each amniotic fluidsample was taken from the freezer and warmed to 20 C. Samples were thentransferred into a 2 mm diameter glass tube which and placed into theRaman system. The Raman system was maintained at 20+/−1 C during thecourse of the experiment. A Nd:YAG laser emitting at 1064 nm was focusedonto the amniotic fluid samples. Raman shifted scattering from thesamples was collected by the FT-spectrometer and detected using a cooledNIR detector. The spectra were scanned at 1/sec resulting in an 8 cm-1resolution of shift from 0-3750 cm-1. A total of 1800 scans wereaveraged for each sample. After a Fourier Transform of the rawinterferogram, the data was stored as 1919 data points spanning the0-3750 cm-1 spectral range.

Data Preprocessing

The amniotic fluid RAMAN spectra were preprocessed to reduce the effectsof intensity variations of the laser. In particular, each spectrum wasnormalized to the Raman emission of the Si—OH at 2500 cm-1. Likewise,spectra were smoothed with a 15-point moving average boxcar smoothingfunction to reduce spurious noise in the measurement.

Haar Transform

The Haar transform (HT) is the oldest form of wavelet analysis. Itprojects a given signal onto an orthogonal set of basis functions. Datacontained in a time window of 0<τ<1 is decomposed according to a fatherwavelet φ(τ), a mother wavelet ψ(τ) and a series of daughter waveletsψ_(n,k)(τ), where n and k determine scaling and translationrespectively:

$\begin{matrix}{{\phi(\tau)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} 0} \leq \tau \leq 1} \\0 & {otherwise}\end{matrix} \right.} & (1) \\{{\psi(\tau)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} 0} \leq \tau \leq {1/2}} \\{- 1} & {{{if}\mspace{14mu}{1/2}} \leq \tau \leq 1} \\0 & {otherwise}\end{matrix} \right.} & (2) \\{{{\psi_{n,k}(\tau)} = {\psi\left( {{2^{n}\tau} - k} \right)}},{0 \leq k \leq {2^{n} - 1}}} & (3)\end{matrix}$

Likewise, each daughter wavelet can be decomposed into the sum of twoson wavelets, φ_(nk)(τ), with a corresponding positive and negativeweighting with the associated scaling and translation. It is interestingto note that all daughter wavelets can be decomposed into a sum of sonwavelets, i.e. compressed and shifted versions of the father wavelet.For instance, ψ=φ_(1,0)−φ_(1,1). Thus, the HT can be carried out with abasis set composed only of zeros and ones, which can be implementedexperimentally by spectral filters. To determine the waveletcoefficients, it is useful to represent the wavelets by a matrix. Forexample, the father, mother and first generation of daughter waveletscan be written as A₂:

$\begin{matrix}{A_{2} = \begin{matrix}{\phi(\tau)} & {\psi(\tau)} & {\psi_{1,0}(\tau)} & {\psi_{1,1}(\tau)} \\\left\lbrack \begin{matrix}1 \\1 \\1 \\1\end{matrix} \right. & \begin{matrix}1 \\1 \\{- 1} \\{- 1}\end{matrix} & \begin{matrix}1 \\{- 1} \\0 \\0\end{matrix} & \left. \begin{matrix}0 \\0 \\1 \\{- 1}\end{matrix} \right\rbrack\end{matrix}} & (4)\end{matrix}$where each column corresponds to a wavelet, and each row represents theHaar wavelet values when the time window is broken into 4 equalsegments. Decomposing a Raman signal of 4 equal wavenumber bins intowavelet coefficients is thus reduced to the following matrix mathproblem: a coefficient vector must be calculated such that itsmultiplication to A₂ yields the Raman spectral profile. Waveletcoefficients in the resulting vector will be ordered starting from thelowest resolution wavelet (father wavelet) and progressing to higherspectral resolution. Matrix A₂ can be expanded to include furthergenerations of daughter wavelets or son wavelets, thereby extending theanalysis to higher frequency levels.

More in-depth information on the Haar transform can be found in A.Graps, “An Introduction to Wavelets,” IEEE Comput. Sci. Eng. 2, 50-61(1995), E. Aboufadel and S. Schlicker, Discovering Wavelets (John Wiley& Sons Inc., NY, 1999), and in J. S. Walker, A Primer on Wavelets andtheir Scientific Applications (Chapman & Hall, Boca Raton, 1999).

Computing the Haar coefficients of the distributions collectedexperimentally using the FTNIR Raman instrument was the first step inthe data analysis. The son wavelete Haar transform calculation wascarried out by a custom program written in Matlab (The MathWorks Inc.,Natick, Mass.) which iteratively calculated sums and differences.Computation required the length of the input data to be a power of 2long. Coefficients for a maximum of 1024 Haar son wavelets wereobtained, ordered from low resolution to high spectral resolution.

Stepwise Multilinear Regression

Inverse least squares regressions can be used to estimate the extrinsicparameters of a given sample from the preprocessed Raman spectra.However, it is probable that not all 1024 wavelets are needed, since theHT gives a sparse representation of the signal. The stepwise multilinearalgorithm is an established method of choosing the subset of variablesmost correlated to a quantity of interest. The general goal of thegenetic algorithm was to identify the combination of wavelets that bestdescribes a given data set according to equation 5:Y=α ₀+α₁ X ₁+α₂ X ₂+ . . . α_(n) X _(n)  (5)where Y is the dependent variable (Birthweight), X₁, X₂, . . . , X_(n)are independent variables (i.e. wavelet coefficients), and α₀, α₁, . . ., α_(n) are the coefficients determined from a set of X's by inverseleast squares regression. The combination of wavelets that bestestimated Y were determined according to the following scheme:

-   -   1. Set the range of HT coefficients that the Stepwise uses.    -   2. Choose the number of wavelets to include in the model.    -   3. Evaluate the fitness of each model.    -   4. Repeat steps 2-4 with an increasing number of wavelets        included in the model.    -   5. Choose the optimal number of variables.    -   6. Evaluate the model using an independent data set.    -   7. Repeat steps 1-6 changing the range of HT coefficients used        by the Stepwise method.

1. Setting the range of HT coefficients that the Stepwise MLR uses: Thegoal of the STEPMLR was to determine a small subset of waveletscorrelated to the birthweight. Likewise, low-resolution (largewavelength range) components were preferable in view of developingidentifying spectral components associated with fetal development andfor simplified instrumentation in the future. Thus, in addition toallowing the Stepwise method to choose amongst all Haar son wavelets tooptimize the estimation, the algorithm was also run with only waveletsof spectral resolution lower than 512, 256,128, 64 and 32 wavelets.

2. Choosing the number of wavelets to include in the model: Start withone wavelet, i.e. one X in equation 5, and increase progressively. Themaximum number of wavelets was set according to the number of waveletcoefficients available for Stepwise selection. In all cases the maximumnumber of wavelets to use was set to 10.

3. Evaluating the fitness of each individual: For each model in thepopulation, the coefficients α₁ to α_(n) of equation 5 were calculatedby inverse least squares regression using the calibration set with knownvalues of absorption or scattering determined from sample preparation.Estimates of birthweight were obtained by applying equation 5 with thedetermined α_(n) parameters and the Haar coefficients of the test set,and a standard error of calibration (SEC) was calculated. Thus a smallerSEC was associated with a better model.

4. Repeat steps 2-3 with an increasing number of wavelets included inthe model: The maximum number of wavelets was chosen in step 2.

5. Choosing the optimal number of variables: A prediction error sum ofsquares (PRESS) plot was generated by plotting SEC vs. the number ofwavelets in the model. Let h designate the number of wavelets in themodel with the minimum PRESS value. The model selected was the one withthe fewest number of wavelets such that PRESS for that model was notsignificantly greater than PRESS for the model with h wavelets, based onan f-test at the 95% confidence level.³⁴

6. Evaluating the model using an independent data set: The “optimal”model was evaluated by estimating the birthweight values of anindependent data set, the validation set, with the calibrationcoefficients from the calibration set. R² and the coefficient ofvariation (C.V.) were used as indicators of the validity of the model.

Results

Three separate calibrations were made for estimating birthweight fromthe amniotic fluid RAMAN Spectra. First, spectra associated with all ofthe samples were used to estimate the birthweight. The results are shownin FIG. 1 b. Estimation of birthweight within 500 grams for all samplewere achieved. Significantly better results were obtained when thesamples were subdivided into groups from <3500 grams and >3500 grams.Results of these two calibrations are shown in FIGS. 1 d and 1 f. As canbe seen, estimations with approximately 200 grams error were found withonly one outlier for each group. This is significantly better than anycurrent method.

Second and Third Embodiments—In Situ Probe Including OpticalSpectrometer

In the second embodiment, endo-vaginal spectral measurements are takenat the time of routine ultrasound which ranges from 2-5 times during thecourse of pregnancy. Early measurements present the opportunity fortherapeutic or nutritional intervention. However, sensitivity of earlygestational measurements may be reduced by the thickness of cervicaltissue (˜4 mm). In contrast, endo-vaginal measurements made later inpregnancy provide less interference from cervical tissue (<1 mm), as itthins throughout pregnancy, but there is less opportunity for medicalintervention. It will be appreciated that ultrasound images of theamniotic sac may be used to help arranging the probe to direct orconfirm that the probe will measure the amniotic fluid withoutinterference of the fetus. If desired, the optical spectrometer may beincorporated into an ultrasound endo-vaginal probe.

The spectral regions which are critical in birth weight prediction forin situ measurements, in particular for estimations of IUGR andmacrosomia can be expected to be slightly different from those obtainedin the above described ex vivo, frozen samples due to interveningtissue, temperature and AMF pH. The specific regression model developedat different gestational time-points is to be compared to “gold”standard measurements obtained by ultrasound.

A regression relationship can be found between Raman spectra and birthweight using endo-vaginal NIR measurements. Likewise, the opticalattenuation spectrum in the NIR or IR range can also be measured andcorrelated for predicting birthweight, and in accordance with thepresent invention other medical conditions. Raman scatteringmeasurements provide good analytical information about amniotic fluid,however, optical attenuation or absorption measurements are expected tobe more efficient in cases where measurement is to be done in situ andthe depth of measurement of the amniotic fluid may render Ramanmeasurements more difficult.

As shown in FIG. 2, the probe has a tip with an optical source and anoptical detector. In the presently preferred embodiment, optical fibersrelay light between a remote source and detector to the tip, althoughintegration of a suitable source and detector into the probe tip isalternatively desirable.

As shown in FIG. 3, the optical probe is operatively connected to aspectrum analyzer that control the optical source or sources anddetector or detectors to obtain the desired spectral information. Asstated, such analyzer is a Raman scatter analyzer. The resultingspectrum data is received by a correlator that calculates a medicalcondition risk value based on calibration data that specifies how thespectral information is to be correlated to the risk value. It will beappreciated that either additionally or alternatively, the correlationmay be performed to determine a value for concentration of a biochemicalmarker or other constituent of the amniotic fluid.

In the third embodiment, shown in FIGS. 4 and 5, the probe to be adaptedwould have a non-invasive tip with an optical source and two opticaldetectors. As illustrated, the first detector “sees” a significantlydifferent pathlength through the tissue between near tissue and deepertissue, while the second detector “sees” less path difference.Subtraction of intensity data measured by the two detectors can thusyield information about deeper tissue, namely the amniotic fluid. Theprobe is adapted to be in optical contact with abdomen of the patient.In the case of an endo-vaginal probe, the device of FIG. 4 may also bedesirable, however, the depth of the tissue or fluid to be measured isnot as great, and thus the separation between first and second detectorsneed not be as great.

More specifically, in the case of a NIR-Raman system suitable forendo-vaginal measurements, a commercially available NIR-Raman systemavailable from Ocean Optics may be adapted for the amniotic fluidmeasurements. The laser for the system can be a low power (50 mW) laserat 785 nm. Choice of the lower wavelength as compared to the 1064 nmused in the first embodiment is desirable since scattering isproportional to λ-4 and will allow low optical power to be used for thenon-invasive, in situ measurements. Likewise, this near infrared regionwill readily transmit through the cervical tissue expected in themeasurements. The detector for the Raman spectrometer is a highsensitivity cooled CCD detector which provides a robust system forportable use. Our previous measurements suggest that relatively lowresolution Raman spectra are sufficient for regression modeling of birthweight. Adjusting the entrance slit for the spectrograph provides aconvenient means to optimize resolution and signal intensity for the insitu measurements. From our preliminary measurement, in vivo spectralacquisitions are expected to take approximately 3 minutes.

Both laser excitation and Raman scattering are transmitted to thepatient by means of custom optical fiber bundles. The bundle consists ofseparate illumination and collection fibers which are focused to thesame location in the tissue. We have shown that this confocal opticalarrangement can be used to isolate precise locations in tissue for threedimensional quantitative measurement. At the distal end of theillumination fiber a small short wavelength pass, optical filter, isplaced to remove unwanted scattering from the illuminating fiber. Asecond long wavelength pass filter is placed in front of the collectionfiber to isolate the Raman shifted signal transmitted to thespectrograph. The diameter of the fiber bundle is less than 2 mm. Theconfocal optical probe is only 2 mm by 5 mm at the ultrasound scan head.

The fiber bundle is attached to a low cost ultrasound imaging systemequipped with an endo-vaginal ultrasound scan head (Medison A-600) sothat in vivo sampling locations can be determined. Using mechanicalindents on the endo-vaginal probe, the fiber optics are clipped withTeflon retaining rings to maintain a known sampling location. Inpatients, condoms are slipped over the ultrasound/optical probe toprovide a sterile environment. Location of spectral acquisition isdetermined using a tissue phantom. In addition to directing the locationof spectral measurements, endo-vaginal ultrasound images will provideinformation about the geometry of the uterus and the membrane which willbe useful for comparisons of the spectra. In addition to constructionand calibration of the system with known composition samples, spectra ofthe purified constituents present in significant quantities in amnioticfluid can be used to provide reference spectra for the constituents sothat comparisons between the spectral regions used in the regression canbe made.

It will be appreciated that while the vaginal probe embodiment describedabove is intended for use in women, however, it will be appreciated thatprobes can be adapted for use in other mammals.

It will also be appreciated that non-optical analytical tools maylikewise be used to gather information in situ about the composition ofamniotic fluid that can be correlated to medical condition risk. Forexample, MRS can give detailed analytical information about chemicalcomposition. Physical parameters of amniotic fluid, such as viscositythat can be measured in situ by ultrasound, may also be used eitheralone or in combination with other optical or non-optical analyticaltools to determine risk or measure one or more biological markers. Theamniotic fluid constituents that vary as a function of predicted birthweight are believed to affect viscosity, and thus correlation betweenviscosity and birth weight is expected.

In the case of optical spectrometry, the suitable wavelength region isroughly between 200 nm to 400 μm. Dried samples of amniotic fluid can beanalyzed throughout this range, while whole samples ex vivo, or amnioticfluid in situ is analyzed using wavelengths that are not unduly absorbedby any intervening tissue or the fluid itself. For example, waterabsorbs heavily in the range of 2 μm to 50 μm, and the presence of waterin the amniotic fluid essentially prevents this range from being usedfor in situ measurements.

Fourth and Fifth Embodiments—Monitoring of Gestational Diabetes Mellitus(GDM) Using Repeated Non-Invasive Amniotic Fluid Analysis

Our study goals were fourfold: 1) to describe the prevalence of GDM in apopulation of older women undergoing routine amniocentesis for genetictesting and at higher risk because of age; 2) to show if elevations inamniotic fluid (AF) glucose, insulin or insulin-like-growth-factorbinding protein (IGF BP) 1 pre-existed at the time of routineamniocentesis (range 12-22 wks) in those women diagnosed at 24-28 wkswith GDM; 3) to establish, using multiple regressions, if an associationwith these amniotic fluid indices and later GDM diagnosis existed; and4) to demonstrate, using probability maps, specific amniotic fluidconcentrations for glucose, insulin and IGF BP 1 that were predictive ofincreased risk for GDM.

Design, Recruitment and Consent: From 1998-2002, pregnant womenundergoing routine amniocentesis at St Mary's Hospital Center inMontreal Canada were approached to participate in this prospectivestudy. Signed consents allowed researchers to obtain amniotic fluid fromMontreal Children's Hospital following genetic testing and to access thematernal medical charts. Applying inclusion (singleton pregnancy) andexclusion criteria (multiple births, genetic anomalies) resulted in 1008participants. Medical chart review provided information on GDM status,maternal age, prepregnancy weight and height, ethnicity, parity, andsmoking (n=888-928), fetal weights estimated by ultrasound at 25 wks(n=70) and at 35 wks (n=149) and infant birth weight, gender andgestational age (n=928). Gestational age was based on physicians'estimates using LMP and uniform hospital protocols. Completeness of eachdata subset depended on availability of information in medical chartsand on questionnaires. Ethics approval was obtained from InstitutionalReview Boards of McGill, Montreal Children's Hospital and St Mary'sHospital Centre.

Biochemical Analysis. Amniotic fluid samples, stored at −80° C., wereanalyzed for glucose, insulin and IGF BP 1. Insulin (n=718) was analyzedusing the Beckman Access ultrasensitive assay system, a one stepimmunoenzymatic assay that added a monoclonal anti-insulin conjugate, anantibody coated paramagnetic particles, and a chemiluminescent substrateto the reaction vessel. Insulin is measured to within 0.03-300 ulpmol/L. Glucose (n=662) was analyzed after adapting Abbott Laboratories(North Chicago, Ill.) assay kit (No. 6082) for use with a micro platereader and IGFBP1 (n=876) by ELISA using Diagnostics SystemsLaboratories Inc (DSL kit 10-7800, Webster, Tex.).

Statistical Analysis: All data analysis were performed using SAS(Version 8.02, SAS Inc., Cary, N.C.) with P<0.05 set as the minimum forstatistical significance. All non-normally distributed data weretransformed using square root: prepregnancy weight, BMI, ethnicity,parity, amniocentesis week, smoking, infant birth weight, 35-week fetalweight, and amniotic fluid glucose, insulin and IGF-BP 1. Biochemicalcomparisons between GDM and non-GDM mothers included as covariatesmaternal prepregnancy BMI, ethnicity, parity and week that amniocentesiswas performed. Multiple regression for GDM and birth weight as dependentvariables and with previously established predictors included in themodels were also verified using forwards and backwards regressions. Dueto co-linearity among IGF BP1, insulin and glucose, each was included inseparate regression models. Data for both GDM and non-GDM mothers wereseparately modeled using a mixture of Gaussian distributions thatemployed a program written in Matlab V6.1 (Mathworks, Inc) calledBayesnet by Ian McNabbey of Cambridge University. A postprioriprobability of development of GDM from amniotic fluid biomarkers forinsulin and glucose was calculated using a Bayesian weighting of theGaussian profiles determined from the measured data. A contour map ofthe probability of development of GDM was then determined for variationsin IGF-BP1 and insulin as related to glucose in the AF, as shown inFIGS. 6 and 7, respectively.

Population characteristics: Comparisons between our GDM and non-GDMsub-populations showed that GDM mothers were shorter, had higherprepregnancy weights and BMIs; 54% of our GDM mothers were overweight orobese while only 26% of non-GDM mothers were. Average birth weight was3396±19 g in our healthy non-GDM mothers verses 3515±52 g in our GDMmothers. However, only 16% of GDM offspring and 12% of non-GDM offspringwere >4000 g; using birth-weight-percentiles that correct for gender andgestational age, 23% of infants born to GDM mothers were >90% (LGA)while only 10% were LGA in our non-GDM population. Both classificationsresulted in 3-4% of our GDM mothers giving birth to either IUGR or SGAinfants. Fetal weights did not different by 25 wks but were 134 gramshigher by 35 wks in GDM mothers. Moreover, at 35 wks, gestational age(β-coefficient (β)=215 g) and GDM (β=54 g; p=0.0450), but not BMI,smoking and infant gender, entered as independent predictors of fetalweight. This difference decreased to 119 grams by term, at which time,GDM entered (β-coefficient=165 g) along with smoking (β=−111 g), infantgender (β=124 g) and gestational age (β=135 g), and prepregnancy heightand weight (β=750 and 7.50 respectively) as independent predictors ofinfant birth weight. The occurrence of GDM was 12% in our studypopulation (n=928) of older mothers (37.8±0.1 yrs, 26-45 yr).

Biochemical Measurements: Concentrations of amniotic fluid glucose werehigher while amniotic fluid IGF BP1 was lower in GDM vs. non-GDM mothersdespite inclusion of BMI, ethnicity, parity, and amniocentesis week;interestingly amniotic fluid insulin no longer differed upon inclusionof these covariates. However, we found that all three amniotic fluidbiochemicals entered as independent predictors for GDM, but onlyamniotic fluid IGF-BP 1 entered for birth weight and as a negativepredictor. Using probability maps to visualize the risk of GDM, we wereable to show that if either amniotic fluid glucose or insulin were highand the other concentration low in amniotic fluid, the risk for GDMexceeded 90%. Moreover, low amniotic fluid IGF BP 1 in the presence of ahigh glucose was also associated with >90% risk for GDM.

Our study explored the possibility that concentrations of amniotic fluidglucose, insulin and IGF BP 1 might already be high in womensubsequently diagnosed with GDM, raising the possibility that theseamniotic fluid constituents might act as early prognosticators for GDM.Our findings are revolutionary because we 1) demonstrated that high AFglucose and low IGF BP1 were associated with the later GDM diagnosis inwomen of varying BMI categories and offspring with varying birth weightsand 2) were able to predict by 15 wks gestation, using probabilityplots, the risk for subsequent diagnosis of GDM for each AF glucose,insulin and IGF BP 1 concentration. Our GDM risk profile assessmentusing amniotic fluid samples obtained at the time of routineamniocentesis for genetic testing preceded current screening anddiagnosis protocols by 10 wks, was based on the presence of highamniotic fluid insulin and glucose concentrations measured earlier inpregnancy and demonstrated that the developing fetus was being exposedto a glucose-enriched environment much earlier in GDM moms. Currentprotocols screen for higher maternal BMI, and effectively attempt tominimize higher birth weights by decreasing fetal abdominalcircumference, macrosomia, obesity, in GDM offspring, but do nothing todiminish the fetopathy associated with earlier in-utero glycation andglycosylation of proteins, reported to exist by the third trimester inGDM mothers. With elevations in amniotic fluid glucose much earlier inpregnancy, fetal damage may be greater than previously expected giventhat amniotic fluid glucose can diffuse through the unkeratinized fetalskin until 20-24 wks, and which could lead to exposure of the developingfetal pancreas to early elevations in amniotic fluid glucose thatpredispose to an increased risk of beta cell exhaustion later in lifeand increase risk of adult disease, with higher BMI and greater risk ofdeveloping diabetes and GDM later in life.

Our population had a prevalence of GDM of 12%. This incidence is higherthan that reported by CDA for a multi-ethnic population includingaboriginals (i.e. 8-18%) and greater than that reported by the ADA (7%).This is not surprising given that the average age of our mothersundergoing routine amniocentesis for genetic testing was greater thanthat indicated as a risk factor by both the CDA and ADA (i.e. >25and >35 yrs), but it does provide the first report of the incidence inthis high-risk population of older women. Noteworthy, was the higherpercentages of Asians in the GDM population as compared to the non-GDMpopulation (37% vs. 18%); however, this observation supports otherreports of a higher prevalence of GDM in Asians. Interestingly, GDMoccurred as frequently in women with BMI less than 25 as in those withBMI greater than 25. Since traditional screening approaches emphasizehigh prepregnancy weight as a risk factor, our data could offer someinsight as to why GDM is being underdiagnosed if as many normal andunderweight individuals are as susceptible. Interestingly, most womengave birth to non-macrosmic offspring where the presence of GDM wasassociated with a 165 gram increase in birth weight. Traditionally thePedersen hypothesis has associated increased birth weight with fetalhyperglycemia and large-for-gestational-age infants, but we alsoobserved that GDM mothers were just as likely to give birth to SGA andAGA. Otherwise, our multiethnic population was non-smoking, with anincidence of IGUR lower than that in the normal population but with anincidence of AGA and macrosomia similar to the Canadian and USpopulations at large.

GDM is currently diagnosed between 24-28 wks. As stated our studyrevealed that AF glucose was already elevated in our GDM sub-populationby 15 wks gestation. Some studies had previously suggested that maternalfasting and 2 h plasma glucose levels were positively associated withbirth weight and that glucose passes freely across the placental barriervia facilitated diffusion, while another study reported that AF insulinwas a better predictor than AF glucose of impaired maternal glucoseintolerance; one study actually showed that AF glucose was notassociated with fetal hyperinsulinism prior to 23 wks gestation. Ouramniotic fluid study, which used a much larger sample size andcontrolled for established confounders, showed in a series of multipleregressions that amniotic fluid glucose and insulin were associated withGDM; birth weight was not predicted by either most likely becauseamniotic fluid insulin measured early in pregnancy is not the primarygrowth factor during early pregnancy but may accumulate during this timeand act later in pregnancy. As for glucose, it predicted GDM but failedto predict birth weight most likely because GDM mothers, who had beentreated, gave birth to infants with a wide range of birth weights.Importantly, however, with the construction of probability maps, thecomplexity of the AF glucose and insulin to predict GDM was evident.Probability contour maps demonstrated that the relationship clearly wasnot linear and if either amniotic fluid glucose or insulin were high andthe other concentration was low, the risk for GDM exceeded 90%. Moreoverthe contour line for each risk profile was non-linear. Therefore whatappears to be most important is for a dissociation to exist between theglucose and insulin values. Wide discrepancies between the two are moreindicative of future emergence of GDM demonstrating that both fetalhyperinsulinism and elevations in amniotic fluid glucose are predictiveof subsequent development of GDM.

Another interesting predictor of GDM was low amniotic fluid IGF BP 1 inthe presence of a high glucose, which was associated with >90% risk forGDM. Previously IGF BP 1 was inversely associated with birth weight, butwe showed using a much larger sample size that lower IGF BP 1 wasassociated with a 54 g increase in fetal weight by 35 wk and a 164 gincrease in birth weight at term. Previous studies have shown this to bedue either to higher levels of growth hormone and/or increased levels ofcirculating IGF 1 or to increased insulin secondary to increased foodingestion, both of which inhibit placental IGF BP 1 production.Increased active IGF 1 would stimulate greater fetal growth during latergestation, and may be responsible for the already established greaterfetal weight by 35 wk in GDM vs. non-GDM offspring.

In conclusion we showed that high AF glucose, insulin and low IGF BP 1predicted GDM and where GDM positively predicted infant birth weight.Because our results convincingly demonstrate that the developing fetusof GDM mothers is already exposed to a ‘diabetogenic risk profile’ inadvance of current GDM diagnosis, earlier screening and intervention iswarranted in order to minimize in utero fetal damage. Additionally, anover-emphasis on BMI as a screening criterion may be responsible formuch of the GDM under diagnosis, since we observed almost 50% of our GDMmothers with BMI <25 and many of the infants were not born large forgestational age.

In the fifth embodiment, the medical condition of GDM is followed usingthe invention by repeating measurements of amniotic fluid duringpregnancy to monitor health and the impact of dietary and/or therapeuticintervention. The method is illustrated in FIG. 8. It will beappreciated that the combination of in situ measurements with dataobtained during amniocentesis is optional, although the confirmation maybe reassuring to physicians not used to interpreting or relying on insitu analysis of amniotic fluid. Likewise, while the invention allowsfor in situ measurement that can be performed before 12 weeks ofpregnancy in women, as illustrated at 10 weeks, and thus well beforeamniocentesis could safely be performed, a physician could also chooseto start monitoring maternal health according to the invention at alater time during pregnancy. While this embodiment is illustrated withthe example of GDM, it will be appreciated that it equally applies tomonitoring fetal health, as in the case of birthweight. Changing diet inthe mother is recognized as being able to influence ultimatebirthweight, and the symptoms of GDM risk. It is believed that earlydetection of risk of developing GDM, and consequently, early change ofdiet will be efficient in reducing actual outcome of developing GDM.Exercise and pharmaceutical intervention may also be applied inaccordance with medical guidelines.

1. A method comprising: a) providing a Raman spectrometer that operatesin the near-infrared range; b) arranging the spectrometer with respectto an amniotic sac of a pregnant mother to acquire a spectrum ofamniotic fluid in situ without insertion of any instrument into saidamniotic sac; c) using said spectrometer to acquire said spectrum; andd) processing said spectrum to predict a risk of developing a medicalcondition in at least one of said pregnant mother and her offspringbased on a predetermined correlation between spectra of amniotic fluidand the likelihood of developing said medical condition.
 2. The methodas claimed in claim 1, wherein said arranging comprises directing saidspectrometer to analyze said amniotic fluid through an abdominal wall.3. The method as claimed in claim 1, wherein said arranging comprisesdirecting said spectrometer to analyze said amniotic fluid through acervix.
 4. The method as claimed in claim 3, further comprisingacquiring ultrasound images of the amniotic sac during said arranging todirect or confirm that said spectrometer will acquire said spectrumwithout interference of said pregnant mother's offspring.
 5. The methodas claimed in claim 1, further comprising: e) determining at least oneof a dietary intervention and a therapeutic intervention in response tofinding that at least one of said pregnant mother and her offspringrisks developing said medical condition.
 6. The method as claimed inclaim 5, wherein steps a) to e) are repeated during said pregnantmother's pregnancy.
 7. The method as claimed in claim 5, wherein saidpregnant mother is human, and steps a) to e) are first performed before12 weeks of said pregnant mother's pregnancy.
 8. The method as claimedin claim 7, wherein an amniocentesis is performed after steps a) to e)are first performed.
 9. The method as claimed in any one of claims 6 to8, wherein steps a) to e) are repeated at least three times during saidpregnant mother's pregnancy.
 10. The method of claim 1, wherein themedical condition is birthweight.
 11. The method of claim 1, wherein themedical condition is gestational diabetes mellitus.
 12. A methodcomprising: a) providing a Raman spectrometer that operates in thenear-infrared range; b) using said spectrometer to acquire a spectrum ofamniotic fluid of a pregnant mother, wherein said optical spectrometeris arranged with respect to the pregnant mother's amniotic sac toacquire a spectrum of said amniotic fluid in situ without insertion ofany instrument into said amniotic sac and wherein the amniotic fluid isanalyzed without processing said fluid to separate or concentrate itscomponents; and c) processing said spectrum to predict a risk ofdeveloping a medical condition in at least one of said pregnant motherand her offspring based on a predetermined correlation between spectraof amniotic fluid and the likelihood of developing said medicalcondition.
 13. The method as claimed in claim 12, wherein said arrangingcomprises directing said spectrometer to analyze said amniotic fluidthrough an abdominal wall.
 14. The method as claimed in claim 12,wherein said arranging comprises directing said spectrometer to analyzesaid amniotic fluid through a cervix.
 15. An apparatus for predicting arisk of developing a medical condition in at least one of a pregnantmother and her offspring, the apparatus comprising: a Raman spectrometerthat operates in the near-infrared range adapted to acquire a spectrumof amniotic fluid from said pregnant mother; an optical coupler adaptedto arrange said optical spectrometer with respect to said pregnantmother's amniotic sac to acquire a spectrum of said amniotic fluid insitu without insertion of any instrument into said amniotic sac; and aprocessing unit for processing said spectrum to predict a risk ofdeveloping a medical condition in at least one of said pregnant motherand her offspring based on a predetermined correlation between spectraof amniotic fluid and the likelihood of developing said medicalcondition.
 16. The apparatus as claimed in claim 15, wherein saidcoupler is adapted to arrange said spectrometer to analyze said fluidthrough an abdominal wall.
 17. The apparatus as claimed in claim 15,wherein said coupler is adapted to arrange said spectrometer to analyzesaid fluid through a cervix.
 18. The apparatus as claimed in claim 15,wherein said coupler is adapted to operate in contact with said pregnantwoman in a position near said amniotic sac.
 19. The apparatus as claimedin claim 15, wherein said optical coupler is comprised within anendo-vaginal probe.
 20. The apparatus as claimed in claim 19, whereinsaid endo-vaginal probe also functions as an ultrasound device.
 21. Theapparatus as claimed in claim 15, wherein said optical coupler comprisesan optical source and two optical detectors.